人工智能带来的复杂性

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2025-05-30 DOI:10.1155/cplx/7656280
Theodore Modis
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引用次数: 0

摘要

本研究旨在定量评估(尽管以任意单位)自火被驯化以来人类系统复杂性的演变。这是通过研究人类进化过程中14个最重要的里程碑(历史上的突破)的时间来实现的。在这里,人工智能被认为是最新的里程碑,其重要性堪比互联网。复杂性被建模为沿着钟形曲线进化,在我们这个时代达到最大值,并很快进入下降轨道。根据这条曲线,下一个具有同等重要性的进化里程碑预计在2050-2052年左右,其复杂性将低于人工智能,但高于核能、DNA和晶体管的里程碑。复杂性曲线的峰值正好与婴儿潮一代的寿命吻合。世界人口增长率的峰值比复杂性的峰值早25年,这大约是一个年轻的男人或女人能够以一种重要的方式增加人类系统复杂性的时间。为了更长久地享受复杂性,尽可能地使复杂性钟形曲线变平,符合社会的利益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Complexity in the Wake of Artificial Intelligence

Complexity in the Wake of Artificial Intelligence

This study aims to evaluate quantitatively (albeit in arbitrary units) the evolution of complexity of the human system since the domestication of fire. This is made possible by studying the timing of the 14 most important milestones—breaks in historical perspective—in the evolution of humans. AI is considered here as the latest such milestone with importance comparable to that of the Internet. The complexity is modeled to have evolved along a bell-shaped curve, reaching a maximum around our times, and soon entering a declining trajectory. According to this curve, the next evolutionary milestone of comparable importance is expected around 2050–2052 and should add less complexity than AI but more than the milestone grouping together nuclear energy, DNA, and the transistor. The peak of the complexity curve coincides squarely with the life span of the baby boomers. The peak in the rate of growth of the world population precedes the complexity peak by 25 years, which is about the time it takes a young man or woman before they are able to add complexity to the human system in a significant way. It is in society’s interest to flatten the complexity bell-shaped curve to whatever extent this is possible in order to enjoy complexity longer.

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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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